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Diffstat (limited to 'src/model.py')
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diff --git a/src/model.py b/src/model.py deleted file mode 100644 index c698f8b..0000000 --- a/src/model.py +++ /dev/null @@ -1,330 +0,0 @@ -""" -Full definition of a GPT Language Model, all of it in this single file. -References: -1) the official GPT-2 TensorFlow implementation released by OpenAI: -https://github.com/openai/gpt-2/blob/master/src/model.py -2) huggingface/transformers PyTorch implementation: -https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py -""" - -import math -import inspect -from dataclasses import dataclass - -import torch -import torch.nn as nn -from torch.nn import functional as F - -class LayerNorm(nn.Module): - """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """ - - def __init__(self, ndim, bias): - super().__init__() - self.weight = nn.Parameter(torch.ones(ndim)) - self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None - - def forward(self, input): - return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5) - -class CausalSelfAttention(nn.Module): - - def __init__(self, config): - super().__init__() - assert config.n_embd % config.n_head == 0 - # key, query, value projections for all heads, but in a batch - self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias) - # output projection - self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias) - # regularization - self.attn_dropout = nn.Dropout(config.dropout) - self.resid_dropout = nn.Dropout(config.dropout) - self.n_head = config.n_head - self.n_embd = config.n_embd - self.dropout = config.dropout - # flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0 - self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') - if not self.flash: - print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0") - # causal mask to ensure that attention is only applied to the left in the input sequence - self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size)) - .view(1, 1, config.block_size, config.block_size)) - - def forward(self, x): - B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd) - - # calculate query, key, values for all heads in batch and move head forward to be the batch dim - q, k, v = self.c_attn(x).split(self.n_embd, dim=2) - k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) - q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) - v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs) - - # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T) - if self.flash: - # efficient attention using Flash Attention CUDA kernels - y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True) - else: - # manual implementation of attention - att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) - att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf')) - att = F.softmax(att, dim=-1) - att = self.attn_dropout(att) - y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs) - y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side - - # output projection - y = self.resid_dropout(self.c_proj(y)) - return y - -class MLP(nn.Module): - - def __init__(self, config): - super().__init__() - self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias) - self.gelu = nn.GELU() - self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias) - self.dropout = nn.Dropout(config.dropout) - - def forward(self, x): - x = self.c_fc(x) - x = self.gelu(x) - x = self.c_proj(x) - x = self.dropout(x) - return x - -class Block(nn.Module): - - def __init__(self, config): - super().__init__() - self.ln_1 = LayerNorm(config.n_embd, bias=config.bias) - self.attn = CausalSelfAttention(config) - self.ln_2 = LayerNorm(config.n_embd, bias=config.bias) - self.mlp = MLP(config) - - def forward(self, x): - x = x + self.attn(self.ln_1(x)) - x = x + self.mlp(self.ln_2(x)) - return x - -@dataclass -class GPTConfig: - block_size: int = 1024 - vocab_size: int = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency - n_layer: int = 12 - n_head: int = 12 - n_embd: int = 768 - dropout: float = 0.0 - bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster - -class GPT(nn.Module): - - def __init__(self, config): - super().__init__() - assert config.vocab_size is not None - assert config.block_size is not None - self.config = config - - self.transformer = nn.ModuleDict(dict( - wte = nn.Embedding(config.vocab_size, config.n_embd), - wpe = nn.Embedding(config.block_size, config.n_embd), - drop = nn.Dropout(config.dropout), - h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]), - ln_f = LayerNorm(config.n_embd, bias=config.bias), - )) - self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) - # with weight tying when using torch.compile() some warnings get generated: - # "UserWarning: functional_call was passed multiple values for tied weights. - # This behavior is deprecated and will be an error in future versions" - # not 100% sure what this is, so far seems to be harmless. TODO investigate - self.transformer.wte.weight = self.lm_head.weight # https://paperswithcode.com/method/weight-tying - - # init all weights - self.apply(self._init_weights) - # apply special scaled init to the residual projections, per GPT-2 paper - for pn, p in self.named_parameters(): - if pn.endswith('c_proj.weight'): - torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer)) - - # report number of parameters - print("number of parameters: %.2fM" % (self.get_num_params()/1e6,)) - - def get_num_params(self, non_embedding=True): - """ - Return the number of parameters in the model. - For non-embedding count (default), the position embeddings get subtracted. - The token embeddings would too, except due to the parameter sharing these - params are actually used as weights in the final layer, so we include them. - """ - n_params = sum(p.numel() for p in self.parameters()) - if non_embedding: - n_params -= self.transformer.wpe.weight.numel() - return n_params - - def _init_weights(self, module): - if isinstance(module, nn.Linear): - torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) - if module.bias is not None: - torch.nn.init.zeros_(module.bias) - elif isinstance(module, nn.Embedding): - torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) - - def forward(self, idx, targets=None): - device = idx.device - b, t = idx.size() - assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}" - pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t) - - # forward the GPT model itself - tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd) - pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd) - x = self.transformer.drop(tok_emb + pos_emb) - for block in self.transformer.h: - x = block(x) - x = self.transformer.ln_f(x) - - if targets is not None: - # if we are given some desired targets also calculate the loss - logits = self.lm_head(x) - loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1) - else: - # inference-time mini-optimization: only forward the lm_head on the very last position - logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim - loss = None - - return logits, loss - - def crop_block_size(self, block_size): - # model surgery to decrease the block size if necessary - # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024) - # but want to use a smaller block size for some smaller, simpler model - assert block_size <= self.config.block_size - self.config.block_size = block_size - self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size]) - for block in self.transformer.h: - if hasattr(block.attn, 'bias'): - block.attn.bias = block.attn.bias[:,:,:block_size,:block_size] - - @classmethod - def from_pretrained(cls, model_type, override_args=None): - assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'} - override_args = override_args or {} # default to empty dict - # only dropout can be overridden see more notes below - assert all(k == 'dropout' for k in override_args) - from transformers import GPT2LMHeadModel - print("loading weights from pretrained gpt: %s" % model_type) - - # n_layer, n_head and n_embd are determined from model_type - config_args = { - 'gpt2': dict(n_layer=12, n_head=12, n_embd=768), # 124M params - 'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024), # 350M params - 'gpt2-large': dict(n_layer=36, n_head=20, n_embd=1280), # 774M params - 'gpt2-xl': dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params - }[model_type] - print("forcing vocab_size=50257, block_size=1024, bias=True") - config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints - config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints - config_args['bias'] = True # always True for GPT model checkpoints - # we can override the dropout rate, if desired - if 'dropout' in override_args: - print(f"overriding dropout rate to {override_args['dropout']}") - config_args['dropout'] = override_args['dropout'] - # create a from-scratch initialized minGPT model - config = GPTConfig(**config_args) - model = GPT(config) - sd = model.state_dict() - sd_keys = sd.keys() - sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')] # discard this mask / buffer, not a param - - # init a huggingface/transformers model - model_hf = GPT2LMHeadModel.from_pretrained(model_type) - sd_hf = model_hf.state_dict() - - # copy while ensuring all of the parameters are aligned and match in names and shapes - sd_keys_hf = sd_hf.keys() - sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')] # ignore these, just a buffer - sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')] # same, just the mask (buffer) - transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight'] - # basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear - # this means that we have to transpose these weights when we import them - assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}" - for k in sd_keys_hf: - if any(k.endswith(w) for w in transposed): - # special treatment for the Conv1D weights we need to transpose - assert sd_hf[k].shape[::-1] == sd[k].shape - with torch.no_grad(): - sd[k].copy_(sd_hf[k].t()) - else: - # vanilla copy over the other parameters - assert sd_hf[k].shape == sd[k].shape - with torch.no_grad(): - sd[k].copy_(sd_hf[k]) - - return model - - def configure_optimizers(self, weight_decay, learning_rate, betas, device_type): - # start with all of the candidate parameters - param_dict = {pn: p for pn, p in self.named_parameters()} - # filter out those that do not require grad - param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad} - # create optim groups. Any parameters that is 2D will be weight decayed, otherwise no. - # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't. - decay_params = [p for n, p in param_dict.items() if p.dim() >= 2] - nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2] - optim_groups = [ - {'params': decay_params, 'weight_decay': weight_decay}, - {'params': nodecay_params, 'weight_decay': 0.0} - ] - num_decay_params = sum(p.numel() for p in decay_params) - num_nodecay_params = sum(p.numel() for p in nodecay_params) - print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters") - print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters") - # Create AdamW optimizer and use the fused version if it is available - fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters - use_fused = fused_available and device_type == 'cuda' - extra_args = dict(fused=True) if use_fused else dict() - optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args) - print(f"using fused AdamW: {use_fused}") - - return optimizer - - def estimate_mfu(self, fwdbwd_per_iter, dt): - """ estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS """ - # first estimate the number of flops we do per iteration. - # see PaLM paper Appendix B as ref: https://arxiv.org/abs/2204.02311 - N = self.get_num_params() - cfg = self.config - L, H, Q, T = cfg.n_layer, cfg.n_head, cfg.n_embd//cfg.n_head, cfg.block_size - flops_per_token = 6*N + 12*L*H*Q*T - flops_per_fwdbwd = flops_per_token * T - flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter - # express our flops throughput as ratio of A100 bfloat16 peak flops - flops_achieved = flops_per_iter * (1.0/dt) # per second - flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS - mfu = flops_achieved / flops_promised - return mfu - - @torch.no_grad() - def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None): - """ - Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete - the sequence max_new_tokens times, feeding the predictions back into the model each time. - Most likely you'll want to make sure to be in model.eval() mode of operation for this. - """ - for _ in range(max_new_tokens): - # if the sequence context is growing too long we must crop it at block_size - idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:] - # forward the model to get the logits for the index in the sequence - logits, _ = self(idx_cond) - # pluck the logits at the final step and scale by desired temperature - logits = logits[:, -1, :] / temperature - # optionally crop the logits to only the top k options - if top_k is not None: - v, _ = torch.topk(logits, min(top_k, logits.size(-1))) - logits[logits < v[:, [-1]]] = -float('Inf') - # apply softmax to convert logits to (normalized) probabilities - probs = F.softmax(logits, dim=-1) - # sample from the distribution - idx_next = torch.multinomial(probs, num_samples=1) - # append sampled index to the running sequence and continue - idx = torch.cat((idx, idx_next), dim=1) - - return idx |